scholarly journals Retinal changes in Alzheimer's disease: Disease Mechanisms to Evaluation perspectives

2018 ◽  
Vol 3 (2) ◽  
pp. 11-13 ◽  
Author(s):  
Mehdi Mirzaei
2019 ◽  
Vol 6 (3) ◽  
pp. 103-112 ◽  
Author(s):  
Sara L. Weisenbach ◽  
Joseph Kim ◽  
Dustin Hammers ◽  
Kelly Konopacki ◽  
Vincent Koppelmans

2018 ◽  
Vol 7 (4) ◽  
pp. 229 ◽  
Author(s):  
Hee Jae Lee ◽  
Hye In Seo ◽  
Hee Yun Cha ◽  
Yun Jung Yang ◽  
Soo Hyun Kwon ◽  
...  

2017 ◽  
Vol 24 (1) ◽  
Author(s):  
Waqar Ahmad ◽  
Bushra Ijaz ◽  
Khadija Shabbiri ◽  
Fayyaz Ahmed ◽  
Sidra Rehman

2013 ◽  
Vol 13 (2-3) ◽  
pp. 163-165 ◽  
Author(s):  
R.S. Osorio ◽  
E. Pirraglia ◽  
T. Gumb ◽  
J. Mantua ◽  
I. Ayappa ◽  
...  

2019 ◽  
Author(s):  
Leon Stefanovski ◽  
Paul Triebkorn ◽  
Andreas Spiegler ◽  
Margarita-Arimatea Diaz-Cortes ◽  
Ana Solodkin ◽  
...  

AbstractIntroductionWhile the prevalence of neurodegenerative diseases associated with dementia such as Alzheimer’s disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi-scale brain modelling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment.MethodsThe Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large-scale structural connectivity-based simulations of whole brain dynamics. We provide proof of concept for a novel approach that quantitatively links the effects of altered molecular pathways onto neuronal population dynamics. As a novelty, we connect chemical compounds measured with positron emission tomography (PET) with neural function in TVB addressing the phenomenon of hyperexcitability in AD related to the protein amyloid beta (Abeta). We construct personalized virtual brains based on individual PET derived distributions of Abeta in patients with mild cognitive impairment (MCI, N=8) and Alzheimer’s Disease (AD, N=10) and in age-matched healthy controls (HC, N=15) using data from ADNI-3 data base (http://adni.lni.usc.edu). In the personalized virtual brains, individual Abeta burden modulates regional inhibition, leading to disinhibition and hyperexcitation with high Abeta loads. We analyze simulated regional neural activity and electroencephalograms (EEG).ResultsKnown empirical alterations of EEG in patients with AD compared to HCs were reproduced by simulations. The virtual AD group showed slower frequencies in simulated local field potentials and EEG compared to MCI and HC groups. The heterogeneity of the Abeta load is crucial for the virtual EEG slowing which is absent for control models with homogeneous Abeta distributions. Slowing phenomena primarily affect the network hubs, independent of the spatial distribution of Abeta. Modeling the N-methyl-D-aspartate (NMDA) receptor antagonism of memantine in local population models, reveals potential functional reversibility of the observed large-scale alterations (reflected by EEG slowing) in virtual AD brains.DiscussionWe demonstrate how TVB enables the simulation of systems effects caused by pathogenetic molecular candidate mechanisms in human virtual brains.


2017 ◽  
pp. 553-565 ◽  
Author(s):  
R. ZAKARIA ◽  
W. M. H. WAN YAACOB ◽  
Z. OTHMAN ◽  
I. LONG ◽  
A. H. AHMAD ◽  
...  

Alzheimer’s disease (AD) is a primary cause of dementia in the middle-aged and elderly worldwide. Animal models for AD are widely used to study the disease mechanisms as well as to test potential therapeutic agents for disease modification. Among the non-genetically manipulated neuroinflammation models for AD, lipopolysaccharide (LPS)-induced animal model is commonly used. This review paper aims to discuss the possible factors that influence rats’ response following LPS injection. Factors such as dose of LPS, route of administration, nature and duration of exposure as well as age and gender of animal used should be taken into account when designing a study using LPS-induced memory impairment as model for AD.


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